Mining and Measurement of Vocational Skills and Their Association Rules Based on Big Data

Jian Wan, Binbin Chen, Huayou Si
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引用次数: 6

Abstract

In recent years, researches on vocational skills are very extensive, most of them concentrated on vocational skills training, effect of vocational skill competition, vocational education and vocational skill identification and so on. But, there is very little research on the inner relationship among vocational skill based on big data. To address the issue, from LinkedIn we first collect tens of thousands of member profiles with vocational skills as empirical data. And then, we dig out high-frequency vocational skills and apply correlation analysis method to explore correlation characteristics of vocational skills. According to our studying, we figure out that there exist a large number of association rules, some of which have very high degrees of confidence, lift, and support. This research reveals the intrinsic association among different vocational skills. These relationships can reflect the general behavior and cognitive rules of human beings. We hope that our research results can provide a theoretical base for other research areas of vocational skills, such as vocational skill training, vocational skill mining, and identification of vocational skills.
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基于大数据的职业技能及其关联规则挖掘与度量
近年来,关于职业技能的研究非常广泛,大多集中在职业技能培训、职业技能竞争的影响、职业教育和职业技能鉴定等方面。但是,基于大数据对职业技能之间内在关系的研究却很少。为了解决这个问题,我们首先从领英上收集了成千上万的具有职业技能的会员资料作为经验数据。然后,挖掘出高频职业技能,运用相关分析方法探究职业技能的相关特征。根据我们的研究,我们发现存在大量的关联规则,其中一些关联规则具有非常高的置信度、举升度和支持度。本研究揭示了不同职业技能之间的内在联系。这些关系可以反映人类的一般行为和认知规律。我们希望我们的研究成果能够为职业技能培训、职业技能挖掘、职业技能识别等其他职业技能研究领域提供理论基础。
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